计算机工程2025，Vol.51Issue(2)：78-85,8.DOI:10.19678/j.issn.1000-3428.0068445
基于改进YOLOv7的MODF端口状态检测算法
MODF Port State Detection Algorithm Based on Improved YOLOv7
摘要
Abstract
In recent years,manual inspection management methods have led to low accuracy in identifying the status of Fiber Optic Distribution Frame(MODF)ports,making it difficult to differentiate between occupied and unoccupied ports.To address the problem of status recognition in MODF port resource management,this study proposes an improved YOLOv7 object-detection model.First,owing to the difficulty in data collection and unbalanced categories,multiple data enhancement methods are used to expand the dataset.In addition,a shared-weight Receptive Field Expansion Module(RFEM)is used in the backbone network to enlarge the receptive field of the port targets and reduce the risk of overfitting during the training process.The F-EMA attention module is proposed to improve the utilization of spatial context information and reduce missed detections and false alarms caused by ports being closed or occluded.The Normalized Gaussian Wasserstein Distance(NWD)loss function is used to replace the Intersection over Union(IoU)measurement,which alleviates the sensitivity to the position deviation of small targets and improves the detection accuracy of dense small objects.The experimental results show that the mAP@0.5 value of the improved model reaches 98.8％,which is 2 percentage points higher than that of the original Yolov7 model,whereas the mAP@0.5∶0.95 value reaches 63.8％,which is 9.5 percentage points higher.This improves the utilization rate of MODF port resources and meets the basic requirements of the intelligent inspection system for the accuracy of port occupancy status recognition.关键词
深度学习/YOLOv7算法/光纤总配线架/损失函数/感受野扩大模块/注意力模块Key words
deep learning/YOLOv7 algorithm/Fiber Optic Distribution Frame(MODF)/loss function/Receptive Field Expansion Module(RFEM)/attention module分类
计算机与自动化引用本文复制引用
胡朝举,郭凤仪..基于改进YOLOv7的MODF端口状态检测算法[J].计算机工程,2025,51(2):78-85,8.基金项目
国家自然科学基金(61502168). （61502168）
